Generation Stage Research Articles

What use of proceeds do family IPOs signal? The influence of family generational stage and family CEO

PurposeThis study aims to investigate the relationship between the family generational stage and the intended use of the Initial Public Offering (IPO) proceeds disclosed in the prospectus. With the aim to explore family business (FB) heterogeneity, it also explores the moderating role of the family CEO.Design/methodology/approachWe draw on signalling theory and hand-collected data on Italian family IPOs that occurred in the period 2000–2020, disentangling the intended use of IPO proceeds as distinguished into three categories. We employ logit regression to test our hypotheses.FindingsAccording to our theoretical predictions, we find that the family generational stage positively affects the disclosure of the investment reason as the intended use of IPO proceeds, while it negatively influences the use for recapitalization and general corporate purposes. The first relationship is moderated by the presence of a family CEO. Our results remain robust with different FBs definitions and a different empirical method.Originality/valueTo the best of the authors’ knowledge, this paper is the first to address the topic of the intended use of IPO proceeds in FBs. In doing so, it opens avenues for future research by enriching an underdeveloped, albeit growing, area of research, that of preparing for the market scrutiny in family IPOs.

Organic geochemistry and 1D-basin modeling in the Taranaki Basin, New Zealand: Implications for deltaic-source rocks of the cenozoic oil and condensate reservoirs

The Taranaki Basin in New Zealand is an area of active exploration for oil and condensate. This research focuses on integrating geochemical characteristics and 1-D basin modeling to Late Cretaceous to Miocene source rock systems, along with oil and condensate data from fifty-seven wells in the Taranaki Basin. The geochemical study reveals that the oil and condensate samples were generated from clay-rich source rocks, containing mixed organic matter, with large amounts of terrestrial organic matter input. These source rocks were deposited in fluvial to fluvio-deltaic environments under oxic conditions. The presence of oleanane in both oil and condensate samples suggests that the source rocks had a significant terrestrial component and deposited during the Late Cretaceous to Cenozoic. Using various biomarker proxies, oil-source rock correlation along with 1-D basin modeling revealed that the oil and condensate were mainly derived from the Late Cretaceous Rakopi and Paleocene Farewell formations at different maturity stages. The oils were generated within the peak-mature oil window, while the condensates primarily resulted from the secondary cracking of oil taking place in the source rock within the gas generation window. This finding is consistent with the 1-D basin modeling results. The model shows that the Paleocene Farewell source rock has achieved the primary stage of oil generation (0.55–0.95 Easy %Ro), contributing to most of the discovered oils in the Cenozoic reservoir rocks. Meanwhile, the Late Cretaceous Rakopi source rock reached the gas window with a higher vitrinite reflectance of more than 1.30 Easy %Ro, indicating greater gas generation potential.

Performance Analysis of Compression Algorithms on Matrix Data: Data Transfer Optimization in Microservices Architectures

With the rapid proliferation of microservices architectures these days, the efficient and fast transfer of large matrix data between services has become a significant challenge. This study presents an analysis aimed at finding solutions to this challenge. The analysis addresses the compression and decompression of large matrix data, focusing on lossless compression algorithms to optimize data transfer without data loss. The study is implemented on an example scenario. This scenario is taken from a project with a microservice architecture. In the example scenario, an image processing service developed in Python programming language generates 640x480 matrix data. After going through a compression algorithm, this data is periodically transferred to a backend service developed in C# programming language. This data is then stored in a database. In the final stage, decompression operations are performed so that this data can be used for reporting. The performance of various compression algorithms in the data compression, database storage and report generation stages is extensively tested. Within the scope of the study, tests were performed using five different compression algorithms (Gzip, Zlib, Deflate, Brotli and Bz2). The results are obtained through performance tests aimed at determining the most optimized end-to-end solution. Analyzing the performance of the compression algorithms on the example scenario, the Brotli algorithm gives the most optimal result in terms of both speed and compression size. This work makes an important contribution to data transfer optimization in microservice architectures and provides a reference for research in this area by presenting the performance analysis of various compression algorithms.

Inheritance Analysis for Quantitative Traits in F2 Populations of Mustard Genotypes Grown Under Heat Stress Environments

Genetic variability is the basic requirement for improving crops which further leads to progress in crop breeding. This study was carried out to determine the inheritance pattern of various agronomically important traits in F2 populations of mustard genotypes. The objective of this study was to determine the genetic variability for agro-morphological traits under control conditions (CC) and heat stress environments (HS) and provide the future strategies to improve this important oilseed crop. The experiment was conducted at the Botanical Garden, Sindh Agriculture University, Tandojam. During 2022-23, a total of 27 genotypes including nine parental mustard genotypes and eighteen F2 progenies were grown under control and heat stress conditions. The experiment was laid out in a randomized complete block design with three replications. The analysis of variance for the genotypes, treatments, and F2 progenies revealed significant differences (P≤0.05) in most of the studied characters, highlighting the importance of genetic variations in the evaluated breeding materials of mustard for stress breeding. When estimating heritability for different characters, including seed yield plant-1, it was found that F2 crosses showed varying levels of heritability for different characteristics, ranging from low to moderate and high. This implies that careful selection may be conducted for traits that have low heritability, especially for seed yield.; however, those characters which showed high heritability would be selected at early generation stage to improve the characters.

Exploration on the improvement of EEG dataset collection and preprocessing methods based on neuroscience theory

This paper analyzes current methods of EEG dataset collection and preprocessing within the framework of neuroscientific theories and proposes potential improvements. Existing datasets are limited because they primarily capture specific stages of neural activity during continuous recitation, missing other crucial stages of language generation. This limitation impacts the performance of deep learning models used for EEG data classification. By examining the multi-stage process of language formation, the paper highlights the need for data collection methods that encompass all stagesconceptualization, grammatical encoding, lexicalization, morphological and phonological encoding, and articulation. The review also discusses the benefits of using nonlinear dynamic system analysis methods over traditional covariance or cross-correlation matrices to better capture the complexities of EEG data. Finally, it suggests treating EEG data at various stages of language formation as multimodal data, as a strategy to improve the comprehensiveness and practicality of EEG datasets, thereby enhancing the accuracy and practical application of EEG-based brain-computer interfaces.

Enhancing new service development effectiveness: the role of customer participation and the moderating effects of empowerment and satisfaction

PurposeThe purpose of this paper is to investigate the role of Customer Participation (CP) in the effectiveness of New Service Development (NSD) by examining the moderating roles of Customer Empowerment (CE) and Customer Satisfaction (CS). The research reduces the risk of failure of the NSD process and/or improves the NSD processes used by companies through the consideration of the results in the practical dimension.Design/methodology/approachThis study investigates the effects of CP at different stages of NSD using a quantitative approach. Data were collected through an online survey questionnaire. Smart PLS was used to analyse the data collected from 509 newsreaders and users of the news agency’s application.FindingsThe model confirmed that CE has an impact on the effectiveness of NSD in the idea generation and commercialization stages, but not in the development stage. Empowerment and customer satisfaction did not influence the three stages of NSD indirectly but directly. The results show that CP, CS and CE do not always have a direct or indirect effect on the development of new services. Therefore, in order to design new service development projects, media news companies need to determine the level of user cooperation.Research limitations/implicationsThe lack of objective data, especially on company performance, forces researchers to use questionnaires to analyse NSD effectiveness. Another limitation is that newspaper users answered the questionnaires, which creates “common method variance.”Practical implicationsResearchers on NSD effectiveness must use questionnaires due to a lack of objective data, especially on company performance. Another limitation is “common method variance” from newspaper users answering questionnaires.Originality/valueThis paper is a response to a perceived need for an examination of how new service development can be successful and effective.

Sex Identification and Male–Female Differences in Ginkgo Biloba Hybrid F1 Generation Seedlings

In exploring the male–female differentiation and differences in the seedling stage of the F1 generation of ginkgo hybrids, an early selection test for the cultivation and research of leafy or medicinal ginkgo industry was conducted, which may provide a basis for boosting the precision of the ginkgo industry. The hybrid F1 generation obtained from the cross-mating was used as material for the determination of growth and development, as well as of the physiology and biochemistry of the monocots, and the male and female differential genes were obtained based on the data of SNPs obtained from the GBS sequencing of the hybrid progeny. In the seedling stage of ginkgo hybrid offspring, male plants had a significantly higher nutrient growth capacity than female plants, while the total flavonoid and terpene lactone contents in female plants were higher than those of the male plants. This result can provide a corresponding theoretical basis for the use of ginkgo germplasm resources, which can make full use of the male and female differences in the seedling stage and maximize the benefits of early sex identification.

DRGNN: Disentangled representation graph neural network for diverse category-level recommendations

Graph neural networks (GNNs) have significantly advanced recommender systems (RecSys) by enhancing their accuracy in complex collaborative filtering scenarios. However, this progress often comes at the cost of overlooking the diversity of recommendations, a factor in user satisfaction. Addressing this gap, this paper introduces the disentangled representation graph neural network (DRGNN). DRGNN integrates diversification into the candidate generation stage using two specialized modules. The first employs disentangled representation learning to separate item preferences from category preferences, thereby mitigating category bias in recommendations. The second module, focusing on positive sample selection, further reduces category bias. This approach not only maintains the high-order connectivity strengths of GNNs but also substantially improves the diversity of recommendations. Our extensive validation of DRGNN on three comprehensive web service datasets, Taobao, Amazon Beauty and MSD, shows that it not only matches the state-of-the-art methods in accuracy but also excels in achieving a balanced trade-off between accuracy and diversity in recommendations.

Registration of six albinism wheat sib lines for genetic aberration of photosynthetic pigments

AbstractPlant biologists have long been fascinated with the abnormal, the monstrous, and the defective. Six sib winter wheat (Triticum aestivum L.) lines with varying types of albinism, ShunMai GAG‐1 (Reg. no. GP‐1090, PI 704106), ShunMai GAG‐2 (Reg. no. GP‐1091, PI 704107), ShunMai GAG‐3 (Reg. no. GP‐1092, PI 704108), ShunMai GAG‐4 (Reg. no. GP‐1093, PI 704109), ShunMai GAG‐5 (Reg. no. GP‐1094, PI 704110), and ShunMai GAG‐6, (Reg. no. GP‐1095, PI 704111), were derived from a cross made in 2013 with unknown pedigree, and were developed using conventional phenotypic selections. Albinism, or stage‐specific albino, is their unique abnormal agronomic characteristics. All their leaves and tillers produced before winter are green, all their spring‐emerging leaves and tillers are albino, and all their summer‐producing leaves turn green again. Their main stems could have at least three albino leaves. In some colder springs, their after‐winter emerging leaves are reddish to pinkish. They may serve as useful experimental materials for addressing a wide range of wheat breeding problems, and for wheat field art too, and may also be important materials for functional studies and eventually lead to the gene discovery. We discuss the possible interactions between plastid genes and nuclear genes and between vernalization genes responsible for the transition from vegetative to generative growth stage and photosynthetic genes.

ToEx: Accelerating Generation Stage of Transformer-Based Language Models via Token-Adaptive Early Exit

ToEx: Accelerating Generation Stage of Transformer-Based Language Models via Token-Adaptive Early Exit

The adaptive consensus reaching process with the ER-PSO interaction mechanism and its application in public charging stations evaluation

BackgroundPublic charging stations (PCSs) have become integral public infrastructure in China, responding to the considerable and rapidly growing demand. Evaluating the service quality of PCSs by a diverse user base has become essential for the effective management of PCSs. ObjectivesIn the group evaluation of PCSs' service quality, which encompasses broad public interests, consensus reaching process (CRP) is commonly utilized to manage group conflicts and achieve evaluation consensus. The intricate interactions among decision makers (DMs) significantly influence the evolution and resilience of collective viewpoints. Against this backdrop, this research aims to develop an adaptive CRP to enhance the discriminatory power of group consensus and improve consensus robustness in the presence of non-cooperative behavior. MethodologyWith the above objectives, this study proposes an adaptive CRP supported by a novel interaction mechanism, consisting of two stages: (1) In the interaction network generation stage, based on the Erdős-Rényi (ER) random network model, innovative interaction signals are generated to guide the generation of an ER-based interaction network, enhancing the dissemination of dominant viewpoints, and constraining the influence of minority opinions. (2) In the decision opinion interaction stage, based on the Particle Swarm Optimization (PSO) algorithm, utilizing novel dual individual fitness functions, the PSO algorithm is employed to formulate the expected updated belief, driving the internal evolution and convergence of opinions. The proposed adaptive CRP is implemented in the group evaluation of PCSs' service quality in Nanjing. FindingsComparative analysis reveals that the proposed CRP offers significant advantages in consensus efficiency, the discriminatory power of group consensus, and consensus robustness in the presence of non-cooperative behavior compared to existing CRPs. NoveltyDiffering from previous CRP research primarily focuses on minimizing adjustment scales, this study emphasizes the adaptive evolution and convergence of opinions, providing a more suitable description of group opinion dynamics. Additionally, from a methodological perspective, this research utilizes the ER network model and the PSO algorithm to support the adaptive CRP at both structural and preference levels, enhancing consensus discriminatory power and robustness.

Source Rock Assessment of the Permian to Jurassic Strata in the Northern Highlands, Northwestern Jordan: Insights from Organic Geochemistry and 1D Basin Modeling

The present study focused on the Permian to Jurassic sequence in the Northern Highlands area, NW Jordan. The Permian to Jurassic sequence in this area is thick and deeply buried, consisting mainly of carbonate intercalated with clastic shale. This study integrated various datasets, including total organic carbon (TOC, wt%), Rock-Eval pyrolysis, visual kerogen examination, gross composition, lipid biomarkers, vitrinite reflectance (VRo%), and bottom-hole temperature measurements. The main aim was to investigate the source rock characteristics of these strata regarding organic richness, kerogen type, depositional setting, thermal maturity, and hydrocarbon generation timing. The Permian strata are poor to fair source rocks, primarily containing kerogen type (KT) III. They are immature in the AJ-1 well and over-mature in the NH-2 well. The Upper Triassic strata are poor source rocks in the NH-1 well and fair to marginally good source rocks in the NH-2 well, containing highly mature terrestrial KT III. These strata are immature to early mature in the AJ-1 well and at the peak oil window stage in the NH-2 well. The Jurassic strata are poor source rocks, dominated by KT III and KT II-III. They are immature to early mature in the AJ-1 well and have reached the oil window in the NH-2 well. Biomarker-related ratios indicate that the Upper Triassic oils and Jurassic samples are source rocks that received mainly terrestrial organic input accumulated in shallow marine environments under highly reducing conditions. These strata are composed mostly of clay-rich lithologies with evidence of deposition in hypersaline and/or stratified water columns. 1D basin models revealed that the Upper Triassic strata reached the peak oil window from the Early Cretaceous (~80 Ma) to the present day in the NH-1 well and from ~130 Ma (Early Cretaceous) to ~90 Ma (Late Cretaceous) in the NH-2 well, with the late stage of hydrocarbon generation continuing from ~90 Ma to the present time. The present-day transformation ratio equals 77% in the Upper Triassic source rocks, suggesting that these rocks have expelled substantial volumes of hydrocarbons in the NH-2 well. To achieve future successful hydrocarbon discoveries in NW Jordan, accurate seismic studies and further geochemical analyses are recommended to precisely define the migration pathways.

System simulation and multi-objective optimization methodology for sustainable municipal solid waste classification management: A case study in China

Cities worldwide are actively implementing waste classification policies to develop efficient waste management systems that address the growing challenges of waste disposal and promote sustainable development. However, selecting tailored waste classification solutions is challenging due to the intricate interrelationships within the system and the complex trade-offs between different management objectives. This study presents a comprehensive methodology that simulates the entire waste classification management system by integrating the stages of waste generation, source classification, and final disposal, all linked by waste quantities and properties. A multi-objective optimization model is employed to identify optimal technical pathways that balance economic, environmental, and energy objectives. This methodology was applied in Zhangjiagang, a medium-sized city in China, revealing that economically optimal solutions focusing on landfill and composting as disposal pathways could exacerbate environmental impacts, with indicators deteriorating by 23.1 %–419.1 % except for freshwater eutrophication potential. In contrast, a multi-objective optimization solution prioritizing incineration and anaerobic digestion achieves environmental and energy benefits valued at 18.7 USD/t, with a modest increase in economic costs of 5.7 USD/t compared to the base year. The unconstrained optimal scenarios achieve net negative environmental costs and recover 2.0 × 106 MJ of energy. The study recommends moderate and dynamically adjusted kitchen waste separation rates depending on the stage of implementation. Additionally, the findings reveal that the “lock-in” effects of mismatched disposal facilities can significantly escalate costs. In conclusion, this study underscores the importance of systematic design and multi-objective optimization, offering a comprehensive methodology for cities aiming to enhance their waste classification strategies.

DKA-RG: Disease-Knowledge-Enhanced Fine-Grained Image–Text Alignment for Automatic Radiology Report Generation

Automatic radiology report generation is a task that combines artificial intelligence and medical information processing, and it fully relies on computer vision and natural language processing techniques. Nowadays, automatic radiology report generation is still a very challenging task because it requires semantically adequate alignment of data from two modalities: radiology images and text. Existing approaches tend to focus on coarse-grained alignment at the global level and do not take into account the disease characteristics of radiology images at fine-grained semantics, which results in the generated reports potentially omitting key disease diagnostic descriptions. In this work, we propose a new approach, disease-knowledge-enhanced fine-grained image–text alignment for automatic radiology report generation (DKA-RG). The method combines global and disease-level alignment, thus facilitating the extraction of fine-grained disease features by the model. Our approach also introduces a knowledge graph to inject medical domain expertise into the model. Our proposed DKA-RG consists of two training steps: the image–report alignment stage and the image-to-report generation stage. In the alignment stage, we use global contrastive learning to align images and texts from a high level and also augment disease contrastive learning with medical knowledge to enhance the disease detection capability. In the report generation stage, the report text generated from the images is more accurate in describing the disease information thanks to sufficient alignment. Through extensive quantitative and qualitative experiments on two widely used datasets, we validate the effectiveness of our DKA-RG on the task of radiology report generation. Our DKA-RG achieves superior performance on multiple types of metrics (natural language generation and clinical efficacy metrics) compared to existing methods, demonstrating that the method can improve the reliability and accuracy of automatic radiology report generation systems.

New Evidences of Fluid Inclusions from Wufeng Formation Reveal Shale Gas Accumulation and Leakage Histories in Northern Guizhou, Southwestern China

Abstract Shale gas accumulation and leakage histories are significant for evaluating the prospects of hydrocarbon exploration. The Wufeng Formation in northern Guizhou is a potential target for shale gas exploration and development. This work selected a typical Well YH1 to conduct detailed fluid inclusion analyses in the Wufeng Formation. We integrated the inclusion results with systematic core description, thin-section microscopic observation, laser Raman spectroscopy, microscopic temperature measurement, and burial–thermal history to reveal shale gas accumulation and leakage histories in northern Guizhou. The results show that the source rocks of the Wufeng Formation occurred in oil generation since the Permian and entered the stage of natural gas generation at the end of the Late Triassic. During the Early Jurassic and Early Cretaceous, shale gas accumulated in the Wufeng Formation reservoirs, and the stratigraphical pressure coefficient ranged from 1.47 to 1.97. Under the influence of the Yanshan orogeny, northern Guizhou experienced a rapid uplift and exhumation since the end of the Early Cretaceous. Under the influence of this tectonic, the stratigraphical pressure plummeted to the normal level, adversely impacting the accumulated shale gas in reservoirs and resulting in leakage events since the end of the Early Cretaceous. The reservoirs were further destroyed and adjusted, and the gas continued to leak because of a long-term and large-scale uplift and exhumation during the Himalayan orogeny. This work clarified that shale gas accumulation and leakage histories are significant for evaluating petroleum exploration.

Geological Conditions of Shale Gas Accumulation in Coal Measures

The shale of different potential layers is studied by using rock pyrolysis analysis, total organic carbon determination (TOC), kerogen microscopic component identification, mineral X-ray diffraction, scanning electron microscopy, and low-temperature nitrogen adsorption experiments. The results are as follows: (1) Shishui Formation of the Lower Carboniferous and Longtan Formation of the Upper Permian are the two most important shale gas reservoirs in the Chenlei Depression. The sedimentary environment of the target shale is a marine land interaction facies coastal bay lagoon swamp sedimentary system. Two sedimentary facies of tidal flat facies, subtidal zone, and lagoon swamp facies are developed. (2) The organic matter types of shale are Type III and II2, with TOC content greater than 1%. The maturity of shale samples is relatively higher (Ro,max is above 2%), which means they have entered the stage of large-scale gas generation. The overall brittle mineral content of the target shale sample is relatively higher (above 40%), which is conducive to artificial fracturing and fracture formation in the later stage, while an appropriate amount of clay minerals (generally stable at 40%) is conducive to gas adsorption. (3) The overall pore structure of the water measurement group and Longtan group is good, with a higher specific surface area and total pore volume (average specific surface area is 12.21 and 8.36 m2/g, respectively), which is conducive to the occurrence of shale gas and has good adsorption and storage potential. The gas content of the water measurement group and the Longtan Formation varies from 0.42 to 5 cm3/g, with an average of 2.1 cm3/g. It indicates that the water measurement group and the Longtan Formation shale gas in the study area have good resource potential.

High sulphur oil of Type II kerogen of the oil shales from Western Central Jordan based on molecular structure and kinetics

Organic rich sedimentary rocks of the Late Cretaceous Muwaqqar Formation from the Lajjun outcrop in the Lajjun Sub-basin, Western Central Jordan were geochemically analyzed. This study integrates kerogen microscopy of the isolated kerogen from 10 oil shale samples with a new finding from unconventional geochemical methods [i.e., ultimate elemental (CHNS), fourier transform infrared spectroscopy and pyrolysis–gas chromatography (Py–GC)] to decipher the molecular structure of the analyzed isolated kerogen fraction and evaluate the kerogen composition and characteristics. The optical kerogen microscopy shows that the isolated kerogen from the studied oil shales is originated from marine assemblages [i.e., algae, bituminite and fluorescence amorphous organic matter] with minor amounts of plant origin organic matter (i.e., spores). This finding suggests that the studied kerogen is hydrogen-rich kerogen, and has the potential to generate high paraffinic oil with low wax content. The dominance of such hydrogen-rich kerogen (mainly Type II) was confirmed from the multi-geochemical ratios, including high hydrogen/carbon atomic of more than 1.30 and high A-factor of more than 0.60. This claim agrees with the molecular structure of the kerogen derived from Py–GC results, which suggest that the studied kerogen is mainly Type II-S kerogen exhibiting the possibility of producing high sulphur oils during earlier stages of diagenesis, according to bulk kinetic modeling. The kinetic models of the isolated kerogen fraction suggest that the kerogen conversion, in coincidence with a vitrinite reflectance range of 0.55–0.60%, commenced at considerably lower temperature value ranges between 100 and 106 °C, which have produced oils during the early stage of oil generation. The kinetic models also suggest that the commercial amounts of oil can generate by kerogen conversion of up to 50% during the peak stage of oil window (0.71–0.83%) at relatively low geological temperature values in the range of 122–138 °C. Therefore, further development of the Muwaqqar oil shale successions is highly approved in the shallowly buried stratigraphic succession in the Lajjun Sub-basin, Western Central Jordan.

Clinical Evaluation of Artificial Intelligence-Enabled Interventions.

Artificial intelligence (AI) health technologies are increasingly available for use in real-world care. This emerging opportunity is accompanied by a need for decision makers and practitioners across healthcare systems to evaluate the safety and effectiveness of these interventions against the needs of their own setting. To meet this need, high-quality evidence regarding AI-enabled interventions must be made available, and decision makers in varying roles and settings must be empowered to evaluate that evidence within the context in which they work. This article summarizes good practices across four stages of evidence generation for AI health technologies: study design, study conduct, study reporting, and study appraisal.

The triggers, motivations, experiments, diffusions, and stakeholders of frugal innovation what we Learn from Thai case studies

Frugal innovators creatively apply existing technologies to address local challenges, effectively reducing costs and optimizing performance. This approach is crucial in developing countries, providing cost-effective solutions to resource scarcity, especially for people experiencing poverty. However, research on the frugal innovation process, especially these innovators' motivations and the stakeholders' roles, remains limited. This article examines the frugal innovation process with a focus on two main aspects: (1) the intrinsic and extrinsic motivations of Thai frugal innovators across the stages of idea generation, experimentation, and application, and (2) the roles played by stakeholders in these processes. Four cases of frugal innovation awarded by Ch. Karnchang Public Company are analyzed and categorized into three groups: non-commercial frugal innovation, commercialized frugal innovation as the primary income source, and commercialized frugal innovation as the supplementing income. Interviews with innovators reveal that motivations vary by commercial purposes and innovation stages. Intrinsic motivation is stronger during experimentation and application stages for non-commercial frugal innovation. In contrast, extrinsic motivation dominates in commercialized innovations. Additionally, stakeholder collaboration in fostering frugal innovation in Thailand is limited. These insights on motivations and stakeholder roles expand understanding of frugal innovation models.

Time-resolved ARPES with probe energy of 6.0 eV and tunable MIR pump at 250 kHz

In this paper, we present a laser source designed specifically for time- and angle-resolved photoemission spectroscopy (TR-ARPES) investigations of light-induced electron dynamics in quantum materials. Our laser source is based on a ytterbium-doped laser that seeds an optical parametric amplifier (OPA) followed by a difference frequency generation (DFG) stage. This configuration enables the generation of tunable near-infrared and mid-infrared laser pulses (1.5 to 8 μm - 0.82 to 0.15 eV) at 250 kHz of repetition rate, serving as the pump for TR-ARPES measurements. The remaining energy of the laser is used to generate the ultraviolet 6 eV probe pulses, which prompt the material to emit photoelectrons. We demonstrate the long-term stability of the source, as well as the characterization of the beam profiles and pulse durations. Additionally, we present preliminary TR-ARPES results obtained on Bi2Te3, a prototypical 3D topological insulator. This paper illustrates the capability of our laser source to probe electronic dynamics in quantum materials.